Review of relevant variables
Date: 2017-05-08
# Attach these packages so their functions don't need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
library(magrittr) # enables piping : %>%
library(dplyr)
library(TabularManifest)# Call `base::source()` on any repo file that defines functions needed below. Ideally, no real operations are performed.
source("./scripts/common-functions.R") # used in multiple reports
source("./scripts/graphing/graph-presets.R") # fonts, colors, themes
source("./scripts/graphing/graph-elemental.R") # graphs to be used in dipslays
source("./scripts/graphing/graph-complex.R") # info displays
# Verify these packages are available on the machine, but their functions need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
requireNamespace("ggplot2") # graphing
# requireNamespace("readr") # data input
requireNamespace("tidyr") # data manipulation
requireNamespace("dplyr") # Avoid attaching dplyr, b/c its function names conflict with a lot of packages (esp base, stats, and plyr).
requireNamespace("testit")# For asserting conditions meet expected patterns.
# requireNamespace("car") # For it's `recode()` function.Exposition
path_input <- "./data-unshared/derived/1-dto.rds" # product of ./manipulation/1-groom-augment.R
# path_output <- ""#Put code in here. It doesn't call a chunk in the codebehind file.# list variables to keep separated for long to wide conversion
variables_static <- c(
"id" #
,"male" # Gender
,"birth_year" # Birth year from RAND longitudinal file
,"birth_month" # Month of birth
,"race" # Race
,"hispanic" # Whether Hispanic
,"cohort" # Cohort based on birth yr
,"edu_years" # Years of Education
,"highest_degree" # Highest Degree
) # static
variables_longitudinal <- c(
"lb_wave" # Leave-behind wave
,"year" # Year
,"lb_65_wave" # Leave-behind wave at age 65 or older
,"interview_date" # Interview data year and month
,"responded" #
,"proxy" #
,"hhres" #
,"countb20r" #
,"shhidpnr" #
,"rmaritalst" #
,"intage_r" #
,"rpartst" #
,"score_loneliness_3" #
,"score_loneliness_11" #
,"snspouse" #
,"snchild" #
,"snfamily" #
,"snfriends" #
,"socialnetwork_total" #
,"close_social_network" #
,"social_support_mean" #
,"social_strain_mean" #
,"social_contact_total" #
,"activity_mean" #
,"activity_sum" #
,"srmemory" #
,"srmemoryp" #
,"wrectoti" #
,"wrectotd" #
,"listassi"
,"mentalstatus_tot" #
,"vocab_total" #
,"dep_total" #
,"healthcond" #
,"exercise" #
) # not staticData
# load the product of 0-ellis-island.R, a list object containing data and metadata
dto <- readRDS(path_input)# dto %>% glimpse()
class(dto)[1] "grouped_df" "tbl_df" "tbl" "data.frame"#str(dto)# rename variables for graphing convenience, Cassandra, please move upstream when stable
ds <- dto %>%
dplyr::rename_(
"id" = "id"
, "male" = "male"
, "birth_year" = "birthyr_rand"
, "birth_month" = "birthmo_rand"
, "race" = "race_rand"
, "hispanic" = "hispanic_rand"
, "cohort" = "cohort"
, "edu_years" = "raedyrs"
, "highest_degree" = "raedegrm"
)
# subset variables of relevance for this project
ds <- ds %>%
dplyr::select_(.dots = c(variables_static, variables_longitudinal)) %>%
as.data.frame() %>%
dplyr::mutate(
male = factor(male, levels = c(1,2), labels = c("Men", "Women"))
,race = factor(race, levels = c(1, 2, 3), labels = c("White","Black","Other") )
,cohort = factor(cohort, levels = c(0, 1, 2, 3, 4, 5, 6), labels = c("Not in any cohort", "Ahead", "Coda", "Hrs", "WarBabies", "Early BabyBoomers", "Mid BabyBoomers") )
,age_at_visit = intage_r
,date_at_visit = interview_date
) %>%
tibble::as_tibble()
ds %>% glimpse(width = 105)Observations: 224,970
Variables: 46
$ id <dbl> 1010, 1010, 1010, 1010, 1010, 1010, 2010, 2010, 2010, 2010, 2010, 2010,...
$ male <fctr> Men, Men, Men, Men, Men, Men, Women, Women, Women, Women, Women, Women...
$ birth_year <dbl> 1938, 1938, 1938, 1938, 1938, 1938, 1934, 1934, 1934, 1934, 1934, 1934,...
$ birth_month <dbl> 2, 2, 2, 2, 2, 2, 10, 10, 10, 10, 10, 10, 1, 1, 1, 1, 1, 1, 9, 9, 9, 9,...
$ race <fctr> White, White, White, White, White, White, White, White, White, White, ...
$ hispanic <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ cohort <fctr> Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, ...
$ edu_years <dbl> 16, 16, 16, 16, 16, 16, 8, 8, 8, 8, 8, 8, 12, 12, 12, 12, 12, 12, 16, 1...
$ highest_degree <dbl> 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 5,...
$ lb_wave <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, 2, NA, NA, N...
$ year <fctr> 2004, 2006, 2008, 2010, 2012, 2014, 2004, 2006, 2008, 2010, 2012, 2014...
$ lb_65_wave <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, 2, NA, NA, N...
$ interview_date <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2004.500, 2006.167, 200...
$ responded <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,...
$ proxy <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0, 0, 0, 0,...
$ hhres <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2, 2, 2, 2,...
$ countb20r <dbl> NaN, NaN, NaN, NaN, NaN, NA, NaN, NaN, NaN, NaN, NaN, NA, 2, 2, 2, 2, 2...
$ shhidpnr <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3020, 3020,...
$ rmaritalst <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1, 1, 1, 1,...
$ intage_r <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 68, 70, 72,...
$ rpartst <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0, 0, 0, 0,...
$ score_loneliness_3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 2.000000, NaN, 2.0...
$ score_loneliness_11 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.909091, N...
$ snspouse <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 1, NaN, 1, NaN, NA...
$ snchild <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 1, NaN, 1, NaN, NA...
$ snfamily <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 1, NaN, 1, NaN, NA...
$ snfriends <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 1, NaN, 1, NaN, NA...
$ socialnetwork_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, 4, NA, NA, N...
$ close_social_network <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 19, 0, 0, 0, 11, 0, 12, 0...
$ social_support_mean <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 11.750...
$ social_strain_mean <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 4.0000...
$ social_contact_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28, NA, 27, NA, NA,...
$ activity_mean <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 2.65, NaN,...
$ activity_sum <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 53, NaN, N...
$ srmemory <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, 2, 3, 2, 2, NA, 2, 2...
$ srmemoryp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, 2, 2, 1, 2, NA, 2, 1...
$ wrectoti <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, 3, 5, 4, 2, NA, 6, 5...
$ wrectotd <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, 3, 7, 3, 2, NA, 6, 6...
$ listassi <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 31, 21, 11, 1, NA, 2...
$ mentalstatus_tot <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8, 9, 9, 7, 6, NA, 9, 9...
$ vocab_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, 5, NaN, 6, NaN, NA...
$ dep_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0, NA, 0, 0...
$ healthcond <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, NA, 2, 2...
$ exercise <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9, 10, 12, 11, 6, NA, 1...
$ age_at_visit <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 68, 70, 72,...
$ date_at_visit <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2004.500, 2006.167, 200...ds %>% names_labels() name label
1 id hhidpn: hhold id + person number /num
2 male <NA>
3 birth_year <NA>
4 birth_month <NA>
5 race <NA>
6 hispanic <NA>
7 cohort <NA>
8 edu_years <NA>
9 highest_degree <NA>
10 lb_wave <NA>
11 year <NA>
12 lb_65_wave <NA>
13 interview_date <NA>
14 responded inw7: =1 if respondent w7
15 proxy r7proxy:w7 whether proxy interview
16 hhres h7hhres:w7 number of people in hh
17 countb20r r7bwc20: w7 backwards count from 20
18 shhidpnr s7hhidpn:w7 spouse hhidpn
19 rmaritalst r7mstat:w7 r marital status
20 intage_r r7agey_e:w7 r age (years) at ivw endmon
21 rpartst r7mpart:w7 r partnered
22 score_loneliness_3 <NA>
23 score_loneliness_11 <NA>
24 snspouse Q7. LIVE WITH SPOUSE OR PARTNER
25 snchild Q10. HAVE ANY CHILDREN
26 snfamily Q14. HAVE ANY OTHER IMMEDIATE FAMILY
27 snfriends Q18. HAVE ANY FRIENDS
28 socialnetwork_total <NA>
29 close_social_network <NA>
30 social_support_mean <NA>
31 social_strain_mean <NA>
32 social_contact_total <NA>
33 activity_mean <NA>
34 activity_sum <NA>
35 srmemory RATE MEMORY
36 srmemoryp RATE MEMORY PAST
37 wrectoti NUMBER GOOD - IMMEDIATE
38 wrectotd NUMBER GOOD - DELAYED
39 listassi D104 WORD LIST ASSIGNMENT
40 mentalstatus_tot <NA>
41 vocab_total <NA>
42 dep_total <NA>
43 healthcond <NA>
44 exercise <NA>
45 age_at_visit r7agey_e:w7 r age (years) at ivw endmon
46 date_at_visit <NA># replace static variables with NA for those interview_dates that are NA
# this means that the person did not have any observation in that wave
# However, this is a temp fix. Address it upstream (possibly during elongation)
# these rows are the ones we've created, so we remove them now
ds %>% distinct(id) %>% count() # n = 37495# A tibble: 1 × 1
n
<int>
1 37495ds <- ds %>% dplyr::filter(!is.na(interview_date))
ds %>% distinct(id) %>% count() # n = 28225# A tibble: 1 × 1
n
<int>
1 28225# # select a single case for inspection
# ids <- sample(unique(ds$id),1)
# #What does data look like for variables that do not change with time?
ds %>% select_(.dots = variables_static) # A tibble: 117,151 × 9
id male birth_year birth_month race hispanic cohort edu_years highest_degree
<dbl> <fctr> <dbl> <dbl> <fctr> <dbl> <fctr> <dbl> <dbl>
1 3010 Men 1936 1 White 0 Hrs 12 2
2 3010 Men 1936 1 White 0 Hrs 12 2
3 3010 Men 1936 1 White 0 Hrs 12 2
4 3010 Men 1936 1 White 0 Hrs 12 2
5 3010 Men 1936 1 White 0 Hrs 12 2
6 3020 Women 1938 9 White 0 Hrs 16 5
7 3020 Women 1938 9 White 0 Hrs 16 5
8 3020 Women 1938 9 White 0 Hrs 16 5
9 3020 Women 1938 9 White 0 Hrs 16 5
10 3020 Women 1938 9 White 0 Hrs 16 5
# ... with 117,141 more rows#How many distinct values are there for each static variable?
set.seed(42)
ds %>%
select_(.dots = variables_static) %>%
# filter(id %in% sample(unique(id),100)) %>%
summarize_all(n_distinct) %>%
t() [,1]
id 28225
male 3
birth_year 91
birth_month 12
race 4
hispanic 3
cohort 7
edu_years 19
highest_degree 9#How many distinct values are there for variables that change over time?
ds %>%
select_(.dots = variables_longitudinal) %>%
summarize_all(n_distinct) %>%
t() [,1]
lb_wave 6
year 6
lb_65_wave 5
interview_date 87
responded 1
proxy 2
hhres 15
countb20r 5
shhidpnr 19805
rmaritalst 9
intage_r 87
rpartst 2
score_loneliness_3 10
score_loneliness_11 83
snspouse 3
snchild 3
snfamily 3
snfriends 4
socialnetwork_total 6
close_social_network 58
social_support_mean 56
social_strain_mean 241
social_contact_total 55
activity_mean 90
activity_sum 90
srmemory 7
srmemoryp 5
wrectoti 12
wrectotd 12
listassi 5
mentalstatus_tot 10
vocab_total 13
dep_total 11
healthcond 9
exercise 15Development
This section will contain a close up examination of relevant variables, one by one.
Static
This section focuses on variables with values that do not change with time.
id
# How many respondents are in the sample?
ds %>% distinct(id) %>% count()# A tibble: 1 × 1
n
<int>
1 28225ds %>% group_by(id) %>% summarize(n=n())# A tibble: 28,225 × 2
id n
<dbl> <int>
1 3010 5
2 3020 6
3 10001010 6
4 10003030 6
5 10004010 4
6 10004040 6
7 10013010 6
8 10013040 6
9 10038010 6
10 10038040 6
# ... with 28,215 more rowsmale
# what is the gender composion of the sample?
ds %>% group_by(male) %>% summarize(n=n())# A tibble: 3 × 2
male n
<fctr> <int>
1 Men 48415
2 Women 68729
3 NA 7ds %>% histogram_discrete("male")
# what is gender composition over time?
ds %>% count_over_time("year","male")
ds %>% count_over_time("lb_wave","male")Warning: Removed 3 rows containing missing values (position_stack).Warning: Removed 3 rows containing missing values (geom_text).
race
# what is the race compositon of the sample
ds %>% group_by(male) %>% summarize(n=n()) %>% neat("pandoc")| male | n |
|---|---|
| Men | 48415 |
| Women | 68729 |
| NA | 7 |
ds %>% histogram_discrete("male")
# what is race composition over time?
ds %>% count_over_time("year","race")
ds %>% count_over_time("lb_wave","race")Warning: Removed 4 rows containing missing values (position_stack).Warning: Removed 4 rows containing missing values (geom_text).
# there may not be enough sample size if split by race
ds %>%
dplyr::filter(lb_wave == 4) %>%
group_by(race) %>%
distinct(id ) %>% count()# A tibble: 3 × 2
race n
<fctr> <int>
1 White 771
2 Black 62
3 Other 35Dynamic
Word List Recall
# examine the assignment of word lists over time
ds %>% over_time("year", "listassi")Measure : listassi
2004 2006 2008 2010 2012 2014 <NA>
1 4642 5869 4055 4612 3844 6006 .
11 4614 5255 4071 4621 4868 4961 .
21 4533 3955 4051 4617 5743 3911 .
31 4502 3390 3900 6701 4951 2820 .
NaN 1838 . 1140 1483 1148 1050 .
<NA> . . . . . . . year mean sd count
1 2004 15.86 11.179 18291
2 2006 13.63 10.940 18469
3 2008 15.85 11.133 16077
4 2010 17.52 11.523 20551
5 2012 17.08 10.699 19406
6 2014 13.00 10.759 17698ds %>% over_time("lb_wave", "listassi")Measure : listassi
1 2 3 4 5 <NA>
1 5386 3303 1638 299 1 18401
11 5385 3273 1484 272 . 17976
21 4957 3187 1264 155 . 17247
31 4996 3431 1183 128 . 16526
NaN 536 319 128 14 . 5662
<NA> . . . . . . lb_wave mean sd count
1 1 15.61 11.183 20724
2 2 16.11 11.273 13194
3 3 14.58 11.149 5569
4 4 12.31 10.561 854
5 5 1.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, wrectoti, wrectotd, listassi
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "wrectoti",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "wrectoti",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "wrectotd",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Social Strain
ds %>% summarize_over_time("year", "social_strain_mean") year mean sd count
1 2006 5.35 1.658 7655
2 2008 5.24 1.671 6999
3 2010 5.29 1.652 8276
4 2012 5.24 1.699 7351
5 2014 5.20 1.670 7490ds %>% summarize_over_time("lb_wave", "social_strain_mean") lb_wave mean sd count
1 1 5.38 1.697 17815
2 2 5.21 1.659 13422
3 3 5.06 1.603 5668
4 4 5.01 1.535 865
5 5 3.31 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, social_strain_mean
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "social_strain_mean",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "social_strain_mean",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Loneliness Three Item Short
ds %>% summarize_over_time("year", "score_loneliness_3") year mean sd count
1 2004 1.44 0.529 3220
2 2006 1.49 0.548 7622
3 2008 1.49 0.545 6957
4 2010 1.48 0.544 8213
5 2012 1.49 0.548 7318
6 2014 1.48 0.546 7452ds %>% summarize_over_time("lb_wave", "score_loneliness_3") lb_wave mean sd count
1 1 1.50 0.553 20950
2 2 1.48 0.542 13342
3 3 1.45 0.524 5628
4 4 1.43 0.514 861
5 5 1.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, score_loneliness_3
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "score_loneliness_3",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "score_loneliness_3",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Loneliness Full Scale
# examine the assignment of word lists over time
ds %>% summarize_over_time("year", "score_loneliness_11") year mean sd count
1 2008 1.52 0.430 6942
2 2010 1.53 0.437 8205
3 2012 1.53 0.437 7309
4 2014 1.54 0.443 7451ds %>% summarize_over_time("lb_wave", "score_loneliness_11") lb_wave mean sd count
1 1 1.55 0.448 11463
2 2 1.52 0.431 11960
3 3 1.51 0.427 5624
4 4 1.50 0.429 859
5 5 1.55 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, score_loneliness_11
) %>%
filter(id %in% sample(unique(id),100))
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "score_loneliness_11",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Social Network
ds %>% summarize_over_time("year", "socialnetwork_total") year mean sd count
1 2004 3.38 0.811 3231
2 2006 3.40 0.754 7713
3 2008 3.32 0.800 7058
4 2010 3.33 0.784 8293
5 2012 3.18 0.859 7359
6 2014 3.14 0.882 7488ds %>% summarize_over_time("lb_wave", "socialnetwork_total") lb_wave mean sd count
1 1 3.31 0.814 21152
2 2 3.27 0.817 13459
3 3 3.21 0.851 5664
4 4 3.22 0.827 866
5 5 4.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, socialnetwork_total
) %>%
filter(id %in% sample(unique(id),100))
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "socialnetwork_total",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 7.8661e-31Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 7.8661e-31Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Close Social Network
# examine the assignment of word lists over time
ds %>% summarize_over_time("year", "close_social_network") year mean sd count
1 2004 1.62 4.574 20129
2 2006 3.74 5.960 18469
3 2008 3.70 5.967 17217
4 2010 3.33 5.663 22034
5 2012 3.17 5.569 20554
6 2014 3.48 5.701 18748ds %>% summarize_over_time("lb_wave", "close_social_network") lb_wave mean sd count
1 1 8.98 6.174 21260
2 2 8.88 6.207 13513
3 3 8.89 6.034 5697
4 4 8.85 6.071 868
5 5 5.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, close_social_network
) %>%
filter(id %in% sample(unique(id),100))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "close_social_network",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Activity
# examine the assignment of activity over time
ds %>% summarize_over_time("year", "activity_sum") year mean sd count
1 2010 53.43 12.245 7341
2 2012 53.54 12.265 6064
3 2014 53.11 12.323 6511ds %>% summarize_over_time("lb_wave", "activity_sum") lb_wave mean sd count
1 1 53.78 12.662 4976
2 2 53.18 12.121 9230
3 3 53.14 12.115 4942
4 4 54.23 12.572 767
5 5 51.00 NA 1set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, activity_sum
) %>%
filter(id %in% sample(unique(id),100))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "activity_sum",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
srmemory
ds %>% over_time("year","srmemory")Measure : srmemory
2004 2006 2008 2010 2012 2014 <NA>
1 960 912 855 961 972 900 .
2 4381 4131 3791 4678 4734 4652 .
3 7525 7178 6871 8503 8002 7510 .
4 4231 3974 3714 5088 4588 3848 .
5 1170 991 832 1401 1093 771 .
NaN 1838 1262 1140 1385 1147 1050 .
<NA> 24 21 14 18 18 17 . year mean sd count
1 2004 3.01 0.968 18267
2 2006 3.00 0.956 17186
3 2008 2.99 0.942 16063
4 2010 3.06 0.963 20631
5 2012 3.00 0.952 19389
6 2014 2.94 0.925 17681ds %>% over_time("lb_wave", "srmemory")Measure : srmemory
1 2 3 4 5 <NA>
1 1004 643 311 41 . 3561
2 4722 3361 1481 228 . 16575
3 8547 5736 2497 390 . 28419
4 4995 2890 1113 181 1 16263
5 1348 560 167 14 . 4169
NaN 631 312 122 14 . 6743
<NA> 13 11 6 . . 82 lb_wave mean sd count
1 1 3.05 0.962 20616
2 2 2.95 0.915 13190
3 3 2.88 0.892 5569
4 4 2.88 0.851 854
5 5 4.00 NA 1
Social Support